Navigating dandiset 000728: Visual Coding - Optical Physiology
In [1]:
Copied!
import os
import json
import pathlib
import warnings
from typing import Union, Tuple, Literal
import h5py
import matplotlib
import numpy
import pandas
import pynwb
import remfile
import scipy.stats as scipy_stats
import matplotlib.pyplot as matplotlib_pyplot
from dandi.dandiapi import DandiAPIClient, RemoteAsset, RemoteDandiset
import os
import json
import pathlib
import warnings
from typing import Union, Tuple, Literal
import h5py
import matplotlib
import numpy
import pandas
import pynwb
import remfile
import scipy.stats as scipy_stats
import matplotlib.pyplot as matplotlib_pyplot
from dandi.dandiapi import DandiAPIClient, RemoteAsset, RemoteDandiset
In [2]:
Copied!
%matplotlib inline
%matplotlib inline
Part 1: Getting oriented to the dataset
Alongside this notebook are included two JSON files that contain high-level metadata of the experiments - the conditions under which each was acquired.
Let's take a look at some of the fields in these metadata structures, such as imaging depths, cre lines, areas, and stimuli available in the Visual Coding - Optical Physiology dataset.
In [3]:
Copied!
# You may need to change these paths to the files depending on your current working directory
# These defaults assume the current working directory is the folder containing this notebook
asset_metadata_folder_path = pathlib.Path("asset_metadata")
experiment_containers_metadata_file_path = asset_metadata_folder_path / "experiment_containers_metadata.json"
visual_coding_sessions_metadata_file_path = asset_metadata_folder_path / "visual_coding_sessions_metadata.json"
# You may need to change these paths to the files depending on your current working directory
# These defaults assume the current working directory is the folder containing this notebook
asset_metadata_folder_path = pathlib.Path("asset_metadata")
experiment_containers_metadata_file_path = asset_metadata_folder_path / "experiment_containers_metadata.json"
visual_coding_sessions_metadata_file_path = asset_metadata_folder_path / "visual_coding_sessions_metadata.json"
In [4]:
Copied!
with open(file=experiment_containers_metadata_file_path, mode="r") as fp:
experiment_containers_metadata = json.load(fp=fp)
with open(file=visual_coding_sessions_metadata_file_path, mode="r") as fp:
visual_coding_sessions_metadata = json.load(fp=fp)
with open(file=experiment_containers_metadata_file_path, mode="r") as fp:
experiment_containers_metadata = json.load(fp=fp)
with open(file=visual_coding_sessions_metadata_file_path, mode="r") as fp:
visual_coding_sessions_metadata = json.load(fp=fp)
Start by getting all the areas, which are referred to as "targeted structures".
In [5]:
Copied!
all_targeted_structures = list(set([experiment_container["targeted_structure"] for experiment_container in experiment_containers_metadata]))
all_targeted_structures
all_targeted_structures = list(set([experiment_container["targeted_structure"] for experiment_container in experiment_containers_metadata]))
all_targeted_structures
Out[5]:
['VISrl', 'VISl', 'VISam', 'VISp', 'VISpm', 'VISal']
Then use a similar approach to get all imaging depths, all cre lines, all reporter lines, all stimuli, and all session types
In [6]:
Copied!
all_cre_lines = list(set([experiment_container["cre_line"] for experiment_container in experiment_containers_metadata]))
all_cre_lines
all_cre_lines = list(set([experiment_container["cre_line"] for experiment_container in experiment_containers_metadata]))
all_cre_lines
Out[6]:
['Cux2-CreERT2', 'Fezf2-CreER', 'Scnn1a-Tg3-Cre', 'Rbp4-Cre_KL100', 'Slc17a7-IRES2-Cre', 'Vip-IRES-Cre', 'Nr5a1-Cre', 'Sst-IRES-Cre', 'Ntsr1-Cre_GN220', 'Rorb-IRES2-Cre', 'Pvalb-IRES-Cre', 'Tlx3-Cre_PL56', 'Emx1-IRES-Cre']
In [7]:
Copied!
all_reporter_lines = list(set([experiment_container["reporter_line"] for experiment_container in experiment_containers_metadata]))
all_reporter_lines
all_reporter_lines = list(set([experiment_container["reporter_line"] for experiment_container in experiment_containers_metadata]))
all_reporter_lines
Out[7]:
['Ai94(TITL-GCaMP6s)', 'Ai162(TIT2L-GC6s-ICL-tTA2)', 'Ai93(TITL-GCaMP6f)-hyg', 'Ai93(TITL-GCaMP6f)', 'Ai148(TIT2L-GC6f-ICL-tTA2)']
In [8]:
Copied!
all_session_types = list(set([session["session_type"] for session in visual_coding_sessions_metadata]))
all_session_types
all_session_types = list(set([session["session_type"] for session in visual_coding_sessions_metadata]))
all_session_types
Out[8]:
['three_session_C', 'three_session_A', 'three_session_C2', 'three_session_B']
The following figure is a good reference for describing the main differences between each session type with respect to the visual stimuli used.
1.1 Experiment containers & sessions¶
The experiment container describes a set of 3 imaging sessions performed for the same field of view (ie. same targeted area and imaging depth in the same mouse that targets the same set of neurons).
Each experiment container has a unique session ID. This, together with the subject ID, will allow us to identify the NWB file from which to access the data from.
Let's choose a visual area and Cre line from the lists above.
Using the metadata in this table, we can find all the experiment containers for this area and Cre line.
Using the metadata in this table, we can find all the experiment containers for this area and Cre line.
In [9]:
Copied!
experiments_subset = [
experiment_container
for experiment_container in experiment_containers_metadata
if experiment_container["targeted_structure"] == "VISp" and experiment_container["cre_line"] == "Cux2-CreERT2"
]
experiment_subset_table = pandas.DataFrame(experiments_subset)
experiment_subset_table
experiments_subset = [
experiment_container
for experiment_container in experiment_containers_metadata
if experiment_container["targeted_structure"] == "VISp" and experiment_container["cre_line"] == "Cux2-CreERT2"
]
experiment_subset_table = pandas.DataFrame(experiments_subset)
experiment_subset_table
Out[9]:
| id | imaging_depth | targeted_structure | cre_line | reporter_line | donor_name | specimen_name | tags | failed | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 511510736 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 222426 | Cux2-CreERT2;Camk2a-tTA;Ai93-222426 | [] | False |
| 1 | 511510855 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 229106 | Cux2-CreERT2;Camk2a-tTA;Ai93-229106 | [] | False |
| 2 | 511509529 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 222420 | Cux2-CreERT2;Camk2a-tTA;Ai93-222420 | [] | False |
| 3 | 511507650 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 222424 | Cux2-CreERT2;Camk2a-tTA;Ai93-222424 | [] | False |
| 4 | 702934962 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 382421 | Cux2-CreERT2;Camk2a-tTA;Ai93-382421 | [] | False |
| 5 | 645413757 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 348262 | Cux2-CreERT2;Camk2a-tTA;Ai93-348262 | [] | False |
| 6 | 659767480 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 360565 | Cux2-CreERT2;Camk2a-tTA;Ai93-360565 | [] | False |
| 7 | 511510650 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 222425 | Cux2-CreERT2;Camk2a-tTA;Ai93-222425 | [] | False |
| 8 | 712178509 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 390323 | Cux2-CreERT2;Camk2a-tTA;Ai93-390323 | [] | False |
| 9 | 511510667 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 222420 | Cux2-CreERT2;Camk2a-tTA;Ai93-222420 | [] | False |
| 10 | 524691282 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 243293 | Cux2-CreERT2;Camk2a-tTA;Ai93-243293 | [] | False |
| 11 | 701412138 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 382421 | Cux2-CreERT2;Camk2a-tTA;Ai93-382421 | [] | False |
| 12 | 511510718 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 231584 | Cux2-CreERT2;Camk2a-tTA;Ai93-231584 | [] | False |
| 13 | 511510699 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 225037 | Cux2-CreERT2;Camk2a-tTA;Ai93-225037 | [] | False |
| 14 | 511510779 | 275 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 225036 | Cux2-CreERT2;Camk2a-tTA;Ai93-225036 | [] | False |
| 15 | 511510670 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 225037 | Cux2-CreERT2;Camk2a-tTA;Ai93-225037 | [] | False |
In [10]:
Copied!
number_of_experiment_containers = len(experiment_containers_metadata)
number_of_experiment_containers
number_of_experiment_containers = len(experiment_containers_metadata)
number_of_experiment_containers
Out[10]:
540
Let's look at one experiment container, imaged from Cux2, in VISp, from imaging depth 175 um.
In [11]:
Copied!
experiment_container_id = experiments_subset[0]["id"]
experiment_container_id
experiment_container_id = experiments_subset[0]["id"]
experiment_container_id
Out[11]:
511510736
In [12]:
Copied!
sessions = [
visual_coding_session for visual_coding_session in visual_coding_sessions_metadata
if visual_coding_session["experiment_container_id"] == experiment_container_id
]
sessions_table = pandas.DataFrame(sessions)
sessions_table
sessions = [
visual_coding_session for visual_coding_session in visual_coding_sessions_metadata
if visual_coding_session["experiment_container_id"] == experiment_container_id
]
sessions_table = pandas.DataFrame(sessions)
sessions_table
Out[12]:
| id | imaging_depth | targeted_structure | cre_line | reporter_line | acquisition_age_days | experiment_container_id | session_type | donor_name | specimen_name | fail_eye_tracking | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 501559087 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 103 | 511510736 | three_session_B | 222426 | Cux2-CreERT2;Camk2a-tTA;Ai93-222426 | True |
| 1 | 501704220 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 104 | 511510736 | three_session_A | 222426 | Cux2-CreERT2;Camk2a-tTA;Ai93-222426 | True |
| 2 | 501474098 | 175 | VISp | Cux2-CreERT2 | Ai93(TITL-GCaMP6f) | 102 | 511510736 | three_session_C | 222426 | Cux2-CreERT2;Camk2a-tTA;Ai93-222426 | True |
Let's get the id for the imaging session in this experiment container that used session type B.
In [13]:
Copied!
session_id = [session for session in sessions if session["session_type"] == "three_session_B"][0]["id"]
session_id
session_id = [session for session in sessions if session["session_type"] == "three_session_B"][0]["id"]
session_id
Out[13]:
501559087
Part 2: Streaming all the data for a single session
Start by defining some quick helper functions...
In [14]:
Copied!
def read_dandi_file(*, asset: RemoteAsset) -> pynwb.NWBFile:
"""Remotely load the NWB file from a DANDI asset object."""
# Harmless warnings from PyNWB
warnings.filterwarnings(action="ignore", message="No cached namespaces found in .*")
warnings.filterwarnings(action="ignore", message="Ignoring cached namespace .*")
s3_url = asset.get_content_url()
byte_stream = remfile.File(url=s3_url)
file = h5py.File(name=byte_stream)
io = pynwb.NWBHDF5IO(file=file, mode="r", load_namespaces=True)
nwbfile = io.read()
return nwbfile
def read_dandi_file(*, asset: RemoteAsset) -> pynwb.NWBFile:
"""Remotely load the NWB file from a DANDI asset object."""
# Harmless warnings from PyNWB
warnings.filterwarnings(action="ignore", message="No cached namespaces found in .*")
warnings.filterwarnings(action="ignore", message="Ignoring cached namespace .*")
s3_url = asset.get_content_url()
byte_stream = remfile.File(url=s3_url)
file = h5py.File(name=byte_stream)
io = pynwb.NWBHDF5IO(file=file, mode="r", load_namespaces=True)
nwbfile = io.read()
return nwbfile
In [15]:
Copied!
def read_visual_coding_ophys_file(
*, dandiset: RemoteDandiset, session_id: str, raw_or_processed: Literal["raw", "processed"] = "processed"
) -> pynwb.NWBFile:
"""Load the NWB file from DANDI given only the unique session ID in the Visual Coding - Optical Physiology dataset."""
if raw_or_processed == "processed":
asset = next(asset for asset in dandiset.get_assets() if f"ses-{session_id}" in asset.path and "behavior" in asset.path)
elif raw_or_processed == "raw":
asset = next(asset for asset in dandiset.get_assets() if f"ses-{session_id}" in asset.path and "behavior" not in asset.path)
return read_dandi_file(asset=asset)
def read_visual_coding_ophys_file(
*, dandiset: RemoteDandiset, session_id: str, raw_or_processed: Literal["raw", "processed"] = "processed"
) -> pynwb.NWBFile:
"""Load the NWB file from DANDI given only the unique session ID in the Visual Coding - Optical Physiology dataset."""
if raw_or_processed == "processed":
asset = next(asset for asset in dandiset.get_assets() if f"ses-{session_id}" in asset.path and "behavior" in asset.path)
elif raw_or_processed == "raw":
asset = next(asset for asset in dandiset.get_assets() if f"ses-{session_id}" in asset.path and "behavior" not in asset.path)
return read_dandi_file(asset=asset)
Now we start streaming data directly from the DANDI Archive! Instead of downloading each entire file (usually around 500 MB; and there are 1518 files!), streaming simply requests the minimal amount of data needed to perform an immediate operation.
We begin by connecting a client and fetching the dandiset object.
In [ ]:
Copied!
dandiset_id = "000728"
client = DandiAPIClient()
dandiset = client.get_dandiset(dandiset_id=dandiset_id)
dandiset_id = "000728"
client = DandiAPIClient()
dandiset = client.get_dandiset(dandiset_id=dandiset_id)
Now we can load the NWB file from the dandiset that corresponds to our session ID.
In [17]:
Copied!
nwbfile = read_visual_coding_ophys_file(dandiset=dandiset, session_id=session_id)
nwbfile = read_visual_coding_ophys_file(dandiset=dandiset, session_id=session_id)
Try exploring the file contents interactively by expanding the dropdowns!
In [18]:
Copied!
nwbfile
nwbfile
Out[18]:
root (NWBFile)
session_description: Auto-generated by neuroconv
identifier: 211c0e3c-4b28-4375-b539-cdb71a42851b
session_start_time
2016-02-04 10:25:24-08:00timestamps_reference_time
2016-02-04 10:25:24-08:00file_create_date
0
2024-03-19 15:55:50.497145-04:00stimulus
natural_movie_one_stimulus
resolution: -1.0
comments: no comments
description: The order and timing for presentation of frames from the the natural movie templates.
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
indexed_timeseries
starting_time: nan
rate: nan
resolution: -1.0
comments: no comments
description: A natural movie presented to the subject.
conversion: 1.0
offset: 0.0
unit: n.a.
data
starting_time_unit: seconds
device
description: An ASUS PA248Q monitor used to display visual stimuli.
manufacturer: ASUS
natural_scenes_stimulus
resolution: -1.0
comments: no comments
description: The order and timing for presentation of the natural scene templates.
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
spontaneous_stimulus
description: Mean luminance gray image.
table
| start_time | stop_time | |
|---|---|---|
| id | ||
| 0 | 1047.42969 | 1342.66703 |
static_gratings
description: Parameterizations of visual non-moving gratings shown to the subject.
table
| start_time | stop_time | orientation_in_degrees | spatial_frequency_in_cycles_per_degree | phase | is_blank_sweep | |
|---|---|---|---|---|---|---|
| id | ||||||
| 0 | 51.55381 | 51.80381 | 90.0 | 0.04 | 0.5 | False |
| 1 | 51.78653 | 52.03653 | 150.0 | 0.04 | 0.5 | False |
| 2 | 52.05250 | 52.30250 | 30.0 | 0.02 | 0.0 | False |
stimulus_template
natural_movie_one
starting_time: nan
rate: nan
resolution: -1.0
comments: no comments
description: A natural movie presented to the subject.
conversion: 1.0
offset: 0.0
unit: n.a.
data
starting_time_unit: seconds
device
description: An ASUS PA248Q monitor used to display visual stimuli.
manufacturer: ASUS
natural_scenes_template
description: A collection of natural scenes presented to the subject. Lasted for exactly 7 frames.
images
NaturalScene0
NaturalScene1
NaturalScene10
NaturalScene100
NaturalScene101
NaturalScene102
NaturalScene103
NaturalScene104
NaturalScene105
NaturalScene106
NaturalScene107
NaturalScene108
NaturalScene109
NaturalScene11
NaturalScene110
NaturalScene111
NaturalScene112
NaturalScene113
NaturalScene114
NaturalScene115
NaturalScene116
NaturalScene117
NaturalScene12
NaturalScene13
NaturalScene14
NaturalScene15
NaturalScene16
NaturalScene17
NaturalScene18
NaturalScene19
NaturalScene2
NaturalScene20
NaturalScene21
NaturalScene22
NaturalScene23
NaturalScene24
NaturalScene25
NaturalScene26
NaturalScene27
NaturalScene28
NaturalScene29
NaturalScene3
NaturalScene30
NaturalScene31
NaturalScene32
NaturalScene33
NaturalScene34
NaturalScene35
NaturalScene36
NaturalScene37
NaturalScene38
NaturalScene39
NaturalScene4
NaturalScene40
NaturalScene41
NaturalScene42
NaturalScene43
NaturalScene44
NaturalScene45
NaturalScene46
NaturalScene47
NaturalScene48
NaturalScene49
NaturalScene5
NaturalScene50
NaturalScene51
NaturalScene52
NaturalScene53
NaturalScene54
NaturalScene55
NaturalScene56
NaturalScene57
NaturalScene58
NaturalScene59
NaturalScene6
NaturalScene60
NaturalScene61
NaturalScene62
NaturalScene63
NaturalScene64
NaturalScene65
NaturalScene66
NaturalScene67
NaturalScene68
NaturalScene69
NaturalScene7
NaturalScene70
NaturalScene71
NaturalScene72
NaturalScene73
NaturalScene74
NaturalScene75
NaturalScene76
NaturalScene77
NaturalScene78
NaturalScene79
NaturalScene8
NaturalScene80
NaturalScene81
NaturalScene82
NaturalScene83
NaturalScene84
NaturalScene85
NaturalScene86
NaturalScene87
NaturalScene88
NaturalScene89
NaturalScene9
NaturalScene90
NaturalScene91
NaturalScene92
NaturalScene93
NaturalScene94
NaturalScene95
NaturalScene96
NaturalScene97
NaturalScene98
NaturalScene99
processing
behavior
description: Processed behavioral data.
data_interfaces
BehavioralTimeSeries
time_series
running_speed
resolution: -1.0
comments: no comments
description: Velocity of the subject over time. Mice were positioned on a running disk during the imaging sessions, and a magnetic shaft encoder (US Digital) attached to this disk recorded the running speed of the mouse during the experiment at 60 samples per second. The running speed was down-sampled to match the timing of the 2-photon imaging (30 Hz).
conversion: 1.0
offset: 0.0
unit: cm/s
data
timestamps
timestamps_unit: seconds
interval: 1
ophys
description: Contains processed optical physiology data.
data_interfaces
DfOverF
roi_response_series
DfOverF
resolution: -1.0
comments: no comments
description: The normalized ΔF/F trace calculated using the AllenSDK. Please consult the AllenSDK for details of the calculation.
conversion: 1.0
offset: 0.0
unit: a.u.
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
timestamps_unit: seconds
interval: 1
rois
description: The regions of interest (ROIs) this response series refers to.
table
description: Segmented regions of interest (ROI).
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
DfOverFEvents
resolution: -1.0
comments: no comments
description: Events from the ΔF/F detected using the L0 method from the AllenSDK. Please consult the AllenSDK for more details of the calculation.
conversion: 1.0
offset: 0.0
unit: a.u.
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
timestamps_unit: seconds
interval: 1
rois
description: The regions of interest (ROIs) this response series refers to.
table
description: Segmented regions of interest (ROI).
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
Fluorescence
roi_response_series
Corrected
resolution: -1.0
comments: no comments
description: Fluorescence per region of interest (ROI) from the raw imaging after spatial demixing and subtraction of neuropil background, but prior to dF/F normalization.
conversion: 1.0
offset: 0.0
unit: n.a.
data
timestamps
timestamps_unit: seconds
interval: 1
rois
description: The regions of interest (ROIs) this response series refers to.
table
description: Segmented regions of interest (ROI).
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
timestamp_link
0: processing/ophys/DfOverF/DfOverF/timestamps
1: processing/ophys/DfOverF/DfOverFEvents/timestamps
2: processing/ophys/Fluorescence/Demixed/timestamps
3: processing/ophys/Fluorescence/Neuropil/timestamps
Demixed
resolution: -1.0
comments: no comments
description: Spatially demixed traces of potentially overlapping masks.
conversion: 1.0
offset: 0.0
unit: n.a.
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
timestamps_unit: seconds
interval: 1
rois
description: The regions of interest (ROIs) this response series refers to.
table
description: Segmented regions of interest (ROI).
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
Neuropil
resolution: -1.0
comments: no comments
description: Fluorescence contaminated by background neuropil.
conversion: 1.0
offset: 0.0
unit: n.a.
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
timestamps_unit: seconds
interval: 1
rois
description: The regions of interest (ROIs) this response series refers to.
table
description: Segmented regions of interest (ROI).
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
ImageSegmentation
plane_segmentations
PlaneSegmentation
description: Segmented regions of interest (ROI).
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
SummaryImages
description: Summary images derived from the two-photon calcium imaging.
images
maximum_intensity_projection
ContaminationRatios
description: Pre-calculated statistics quantifying the effectiveness of the neuropil subtraction.
table
| ratios | ratios_rmse | |
|---|---|---|
| id | ||
| 0 | 0.078 | 0.003927 |
| 1 | 0.125 | 0.003921 |
| 2 | 0.188 | 0.003701 |
| 3 | 0.165 | 0.003572 |
... and 170 more rows.
epoch_tags
set()devices
Camera
description: An AVT Mako-G032B camera used to track eye movement and pupil dilation.
manufacturer: Allied Vision
Microscope
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
StimulusDisplay
description: An ASUS PA248Q monitor used to display visual stimuli.
manufacturer: ASUS
imaging_planes
ImagingPlane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
intervals
epochs
description: Coarse grain experiment structure in the alternating presentations of visual stimuli.
table
| start_time | stop_time | stimulus_type | |
|---|---|---|---|
| id | |||
| 0 | 51.55381 | 531.93378 | static_gratings |
| 1 | 561.98893 | 1042.44259 | natural_scenes |
| 2 | 1047.42969 | 1342.66703 | spontaneous |
| 3 | 1342.70028 | 1823.72944 | natural_scenes |
... and 4 more rows.
subject
age: P104D
age__reference: birth
description: Mus musculus in vivo.
genotype: Cux2-CreERT2/wt;Camk2a-tTA/wt;Ai93(TITL-GCaMP6f)/Ai93(TITL-GCaMP6f)
sex: M
species: Mus musculus
subject_id: 495727015
strain: Cux2-CreERT2;Camk2a-tTA;Ai93-222426
epochs
description: Coarse grain experiment structure in the alternating presentations of visual stimuli.
table
| start_time | stop_time | stimulus_type | |
|---|---|---|---|
| id | |||
| 0 | 51.55381 | 531.93378 | static_gratings |
| 1 | 561.98893 | 1042.44259 | natural_scenes |
| 2 | 1047.42969 | 1342.66703 | spontaneous |
| 3 | 1342.70028 | 1823.72944 | natural_scenes |
... and 4 more rows.
experiment_description: For more information, please see http://help.brain-map.org/display/observatory/Allen+Brain+Observatory
session_id: 501559087-StimB
institution: Allen Institute for Brain Science
data_collection: Generated by pipeline Brain Observatory version 3.0.
notes: Container ID: 511510736
Mouse ID (from genotype white paper): 222426
Session type: three_session_B
protocol: 20160204_222426_3StimB
Part 3: Loading specific fields of the data for a single session
Begin by defining some plotting functions to simplify cells throughout the later sections...
In [19]:
Copied!
def plot_image(
*, # "*" character forces keyword argument usage, which makes code much more readable
image: numpy.ndarray,
size: int = 8,
color_map: Union[str, None] = None
) -> None:
"""
Pretty rendering of a simple square grayscale image.
Axis labels and ticks are stripped since their size is viewable from the image array shape.
Also, display the color bar at a good size if color is not gray.
"""
matplotlib_pyplot.figure(figsize=(size, size))
frame = matplotlib_pyplot.gca()
for label in frame.axes.get_xticklabels():
label.set_visible(False)
label.set_fontsize(0.0)
for label in frame.axes.get_yticklabels():
label.set_fontsize(0.0)
label.set_visible(False)
for tick in frame.axes.get_xticklines():
tick.set_visible(False)
for tick in frame.axes.get_yticklines():
tick.set_visible(False)
plot = matplotlib_pyplot.imshow(image, cmap=color_map);
if color_map != "gray":
color_bar = matplotlib_pyplot.colorbar(plot, fraction=0.046, pad=0.04)
color_bar.solids.set_edgecolor("face")
def plot_image(
*, # "*" character forces keyword argument usage, which makes code much more readable
image: numpy.ndarray,
size: int = 8,
color_map: Union[str, None] = None
) -> None:
"""
Pretty rendering of a simple square grayscale image.
Axis labels and ticks are stripped since their size is viewable from the image array shape.
Also, display the color bar at a good size if color is not gray.
"""
matplotlib_pyplot.figure(figsize=(size, size))
frame = matplotlib_pyplot.gca()
for label in frame.axes.get_xticklabels():
label.set_visible(False)
label.set_fontsize(0.0)
for label in frame.axes.get_yticklabels():
label.set_fontsize(0.0)
label.set_visible(False)
for tick in frame.axes.get_xticklines():
tick.set_visible(False)
for tick in frame.axes.get_yticklines():
tick.set_visible(False)
plot = matplotlib_pyplot.imshow(image, cmap=color_map);
if color_map != "gray":
color_bar = matplotlib_pyplot.colorbar(plot, fraction=0.046, pad=0.04)
color_bar.solids.set_edgecolor("face")
In [20]:
Copied!
def plot_traces(
*,
timestamps: numpy.ndarray,
traces: numpy.ndarray,
hide_y_scale: bool = True,
x_axis_label: Union[str, None] = "Time (s)", # Since timestamps in seconds are so common in NWB, just make it the default
y_axis_label: Union[str, None] = None,
padding: float = 2.0,
trace_color: Union[str, None] = None,
reset_figure: bool = True,
figure_shape: Tuple[int, int] = (10, 4),
return_frame: bool = False,
) -> Union[matplotlib.axes.Axes, None]:
"""
Pretty rendering of traces of activity (such as ROI or running speed) over time.
The Y axis of ROI traces is rarely known in practice, so numerical scale doesn't really matter for plotting.
Also, timestamps from NWB will always be in seconds.
"""
if reset_figure:
matplotlib_pyplot.figure(figsize=figure_shape)
if len(traces.shape) == 1:
matplotlib_pyplot.plot(timestamps, traces, color=trace_color)
else:
for trace_index in range(traces.shape[1]):
matplotlib_pyplot.plot(
timestamps,
traces[:, trace_index] + (trace_index * padding), # Add vertical padding to separate each trace
color=trace_color,
)
frame = matplotlib_pyplot.gca()
frame.axes.spines['top'].set_visible(False)
frame.axes.spines['right'].set_visible(False)
frame.axes.spines['bottom'].set_visible(False)
frame.axes.spines['left'].set_visible(False)
if hide_y_scale:
for label in frame.axes.get_yticklabels():
label.set_fontsize(0.0)
label.set_visible(False)
for tick in frame.axes.get_yticklines():
tick.set_visible(False)
matplotlib_pyplot.xlabel(x_axis_label, fontsize=16)
if y_axis_label is not None:
matplotlib_pyplot.ylabel(y_axis_label, fontsize=16)
if return_frame:
return frame
def plot_traces(
*,
timestamps: numpy.ndarray,
traces: numpy.ndarray,
hide_y_scale: bool = True,
x_axis_label: Union[str, None] = "Time (s)", # Since timestamps in seconds are so common in NWB, just make it the default
y_axis_label: Union[str, None] = None,
padding: float = 2.0,
trace_color: Union[str, None] = None,
reset_figure: bool = True,
figure_shape: Tuple[int, int] = (10, 4),
return_frame: bool = False,
) -> Union[matplotlib.axes.Axes, None]:
"""
Pretty rendering of traces of activity (such as ROI or running speed) over time.
The Y axis of ROI traces is rarely known in practice, so numerical scale doesn't really matter for plotting.
Also, timestamps from NWB will always be in seconds.
"""
if reset_figure:
matplotlib_pyplot.figure(figsize=figure_shape)
if len(traces.shape) == 1:
matplotlib_pyplot.plot(timestamps, traces, color=trace_color)
else:
for trace_index in range(traces.shape[1]):
matplotlib_pyplot.plot(
timestamps,
traces[:, trace_index] + (trace_index * padding), # Add vertical padding to separate each trace
color=trace_color,
)
frame = matplotlib_pyplot.gca()
frame.axes.spines['top'].set_visible(False)
frame.axes.spines['right'].set_visible(False)
frame.axes.spines['bottom'].set_visible(False)
frame.axes.spines['left'].set_visible(False)
if hide_y_scale:
for label in frame.axes.get_yticklabels():
label.set_fontsize(0.0)
label.set_visible(False)
for tick in frame.axes.get_yticklines():
tick.set_visible(False)
matplotlib_pyplot.xlabel(x_axis_label, fontsize=16)
if y_axis_label is not None:
matplotlib_pyplot.ylabel(y_axis_label, fontsize=16)
if return_frame:
return frame
In [21]:
Copied!
def apply_epoch_shading(
*,
frame: matplotlib.axes.Axes,
epoch_table: pynwb.file.TimeIntervals,
) -> None:
"""For each epoch, shade the plot based on the stimulus type."""
# Clear previous legend to prevent appending
frame.legend([])
epoch_table_data_frame = epoch_table.to_dataframe()
stimulus_type_to_color = dict(
spontaneous="grey",
static_gratings="fuchsia",
drifting_gratings="lavender",
natural_scenes="orange",
natural_movie_one="green",
natural_movie_two="red",
natural_movie_three="blue",
)
stimulus_type_to_label = dict(
spontaneous="Spontaneous",
static_gratings="Static Gratings",
drifting_gratings="Drifting Gratings",
natural_scenes="Natural Scenes",
natural_movie_one="Natural Movie (1)",
natural_movie_two="Natural Movie (2)",
natural_movie_three="Natural Movie (3)",
)
patches= list()
for stimulus_name in epoch_table_data_frame.stimulus_type.unique():
stimulus = epoch_table_data_frame[epoch_table_data_frame.stimulus_type==stimulus_name]
if len(stimulus) > 0:
patches.append(
matplotlib.patches.Patch(
color=stimulus_type_to_color[stimulus_name],
label=stimulus_type_to_label[stimulus_name],
alpha=0.1,
)
)
for _, row in stimulus.iterrows():
frame.axvspan(
xmin=row.start_time, xmax=row.stop_time, color=stimulus_type_to_color[stimulus_name], alpha=0.1
)
frame.legend(title="Epochs", loc="center left", handles=patches, shadow=True, fancybox=True, bbox_to_anchor=(1, 0.5));
def apply_epoch_shading(
*,
frame: matplotlib.axes.Axes,
epoch_table: pynwb.file.TimeIntervals,
) -> None:
"""For each epoch, shade the plot based on the stimulus type."""
# Clear previous legend to prevent appending
frame.legend([])
epoch_table_data_frame = epoch_table.to_dataframe()
stimulus_type_to_color = dict(
spontaneous="grey",
static_gratings="fuchsia",
drifting_gratings="lavender",
natural_scenes="orange",
natural_movie_one="green",
natural_movie_two="red",
natural_movie_three="blue",
)
stimulus_type_to_label = dict(
spontaneous="Spontaneous",
static_gratings="Static Gratings",
drifting_gratings="Drifting Gratings",
natural_scenes="Natural Scenes",
natural_movie_one="Natural Movie (1)",
natural_movie_two="Natural Movie (2)",
natural_movie_three="Natural Movie (3)",
)
patches= list()
for stimulus_name in epoch_table_data_frame.stimulus_type.unique():
stimulus = epoch_table_data_frame[epoch_table_data_frame.stimulus_type==stimulus_name]
if len(stimulus) > 0:
patches.append(
matplotlib.patches.Patch(
color=stimulus_type_to_color[stimulus_name],
label=stimulus_type_to_label[stimulus_name],
alpha=0.1,
)
)
for _, row in stimulus.iterrows():
frame.axvspan(
xmin=row.start_time, xmax=row.stop_time, color=stimulus_type_to_color[stimulus_name], alpha=0.1
)
frame.legend(title="Epochs", loc="center left", handles=patches, shadow=True, fancybox=True, bbox_to_anchor=(1, 0.5));
3.0 Raw Motion Corrected Movies¶
Most of the sessions in this dataset have the full original motion corrected microscopy data available - that would be around 80 TB if we uncompressed it!
Thankfully, we never need to download the data (unless we want to work offline).
However, since people do like to occasionally download local copies of the processed data, the raw data is stored in separate files that are accessed less frequently.
Let's plot a few frames from the microscopy to see what it looks like.
In [22]:
Copied!
nwbfile_with_raw_imaging = read_visual_coding_ophys_file(dandiset=dandiset, session_id=session_id, raw_or_processed="raw")
nwbfile_with_raw_imaging
nwbfile_with_raw_imaging = read_visual_coding_ophys_file(dandiset=dandiset, session_id=session_id, raw_or_processed="raw")
nwbfile_with_raw_imaging
Out[22]:
root (NWBFile)
session_description: 2-photon recording of visual areas in head-fixed mouse with visual stimulus.
Monitor was positioned 15 cm from the mouse's eye, and spanned 120 degrees x 95 degrees of visual space without accounting for stimulus warping.
Each monitor was gamma corrected using a USB-650 Red Tide Spectrometer (Ocean Optics). Luminance was measured using a SpectroCAL MKII Spectroradiometer (Cambridge Research Systems). Monitors were used at a brightness setting of 30% and contrast at 50%, corresponding to mean luminance of 50 cd/m^2.
identifier: 8bc0aa1c-49f6-4e35-a90b-ddc2b7cc7bc0
session_start_time
2016-02-04 10:25:24-08:00timestamps_reference_time
2016-02-04 10:25:24-08:00file_create_date
0
2024-03-23 01:03:19.871158-04:00acquisition
MotionCorrectedTwoPhotonSeries
resolution: -1.0
comments: no comments
description: Motion corrected fluorescence from calcium imaging recording. Refer to the 'MotionCorrectionShiftsPerFrame' series to see how each frame was shifted.
conversion: 1.0
offset: 0.0
unit: n.a.
data
timestamps
timestamps_unit: seconds
interval: 1
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
timestamp_link
0: acquisition/MotionCorrectionShiftsPerFrame/timestamps
MotionCorrectionShiftsPerFrame
resolution: -1.0
comments: no comments
description: The continuous column (first value of the second axis) and row (second value of second axis) shiftsestimated by motion correction. Actual pixel shifts per axis are the nearest integer to these values.
conversion: 1.0
offset: 0.0
unit: n.a.
data
timestamps (link to acquisition/MotionCorrectedTwoPhotonSeries/timestamps)
timestamps_unit: seconds
interval: 1
epoch_tags
set()devices
Microscope
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
imaging_planes
ImagingPlane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
subject
age: P104D
age__reference: birth
description: Mus musculus in vivo.
genotype: Cux2-CreERT2/wt;Camk2a-tTA/wt;Ai93(TITL-GCaMP6f)/Ai93(TITL-GCaMP6f)
sex: M
species: Mus musculus
subject_id: 495727015
strain: Cux2-CreERT2;Camk2a-tTA;Ai93-222426
experiment_description: For more information, please see http://help.brain-map.org/display/observatory/Allen+Brain+Observatory
session_id: 501559087-StimB
institution: Allen Institute for Brain Science
data_collection: Generated by pipeline Brain Observatory version 3.0.
notes: Container ID: 511510736
Mouse ID (from genotype white paper): 222426
Session type: three_session_B
protocol: 20160204_222426_3StimB
In [23]:
Copied!
motion_corrected_imaging = nwbfile_with_raw_imaging.acquisition["MotionCorrectedTwoPhotonSeries"]
motion_corrected_imaging
motion_corrected_imaging = nwbfile_with_raw_imaging.acquisition["MotionCorrectedTwoPhotonSeries"]
motion_corrected_imaging
Out[23]:
MotionCorrectedTwoPhotonSeries (TwoPhotonSeries)
resolution: -1.0
comments: no comments
description: Motion corrected fluorescence from calcium imaging recording. Refer to the 'MotionCorrectionShiftsPerFrame' series to see how each frame was shifted.
conversion: 1.0
offset: 0.0
unit: n.a.
data
timestamps
timestamps_unit: seconds
interval: 1
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
timestamp_link
0: acquisition/MotionCorrectionShiftsPerFrame/timestamps
In [24]:
Copied!
plot_image(image=motion_corrected_imaging.data[3_000, ...], color_map="gray")
plot_image(image=motion_corrected_imaging.data[3_000, ...], color_map="gray")
That looks pretty noisy! The following sections will examine what the data looks like after it's been thoroughly cleaned.
3.1 Maximum projection¶
This is the projection of the full motion corrected movie. It shows all of the cells imaged during the session.
In [25]:
Copied!
maximum_intensity_projection = nwbfile.processing["ophys"]["SummaryImages"]["maximum_intensity_projection"][:]
maximum_intensity_projection
maximum_intensity_projection = nwbfile.processing["ophys"]["SummaryImages"]["maximum_intensity_projection"][:]
maximum_intensity_projection
Out[25]:
array([[ 296, 308, 306, ..., 2760, 2164, 888],
[ 312, 298, 303, ..., 2741, 1716, 1031],
[ 278, 295, 298, ..., 1426, 929, 574],
...,
[ 347, 348, 366, ..., 407, 403, 410],
[ 335, 357, 398, ..., 317, 354, 362],
[ 308, 288, 306, ..., 293, 316, 318]], dtype=uint16)
In [26]:
Copied!
maximum_intensity_projection.shape
maximum_intensity_projection.shape
Out[26]:
(512, 512)
The slicing operation
[:] is what triggers the data to be sent from the archive. You can even limit the amount of data requested by choosing a subrange!
In [27]:
Copied!
plot_image(image=maximum_intensity_projection, color_map="gray")
plot_image(image=maximum_intensity_projection, color_map="gray")
3.2 PlaneSegmentation¶
These are all of the segmented masks for cell bodies in this experiment. We can view a table summary of these by calling to_dataframe().
In [28]:
Copied!
plane_segmentation = nwbfile.processing["ophys"]["ImageSegmentation"]["PlaneSegmentation"]
plane_segmentation_table = plane_segmentation.to_dataframe()
plane_segmentation_table
plane_segmentation = nwbfile.processing["ophys"]["ImageSegmentation"]["PlaneSegmentation"]
plane_segmentation_table = plane_segmentation.to_dataframe()
plane_segmentation_table
Out[28]:
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0... |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0... |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0... |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0... |
| 517471959 | 517471959 | [[19, 346, 1.0], [19, 347, 1.0], [19, 348, 1.0... |
| ... | ... | ... |
| 517473240 | 517473240 | [[127, 125, 1.0], [127, 126, 1.0], [127, 127, ... |
| 587376723 | 587376723 | [[132, 187, 1.0], [132, 188, 1.0], [132, 189, ... |
| 517472450 | 517472450 | [[129, 87, 1.0], [130, 87, 1.0], [130, 88, 1.0... |
| 517473191 | 517473191 | [[195, 264, 1.0], [195, 265, 1.0], [195, 266, ... |
| 517471925 | 517471925 | [[157, 160, 1.0], [158, 160, 1.0], [158, 161, ... |
174 rows × 2 columns
Notice that each cell in the dataset has two IDs: a local ID specific to this table and session, and a unique global ID.
The global ID can be used to find experiments/session for a specified cell. It is also valuable for finding the matched cell across sessions for the same experiment container (e.g., if you want to compare a cell's response to drifting gratings with its response the natural scenes).
Pixel masks are a data structure that record a list of pixels (width x height) and weights that outline each ROI.
In [29]:
Copied!
pixel_masks = plane_segmentation["pixel_mask"][:]
pixel_masks[0][:10]
pixel_masks = plane_segmentation["pixel_mask"][:]
pixel_masks[0][:10]
Out[29]:
array([(55, 291, 1.), (55, 292, 1.), (56, 290, 1.), (56, 291, 1.),
(56, 292, 1.), (56, 293, 1.), (56, 294, 1.), (56, 295, 1.),
(56, 296, 1.), (57, 289, 1.)],
dtype=[('x', '<u4'), ('y', '<u4'), ('weight', '<f4')])
In [30]:
Copied!
combined_image_masks = numpy.zeros(shape=maximum_intensity_projection.shape)
for pixel_mask in plane_segmentation["pixel_mask"][:]:
for x, y, w in pixel_mask:
combined_image_masks[x,y] += w
combined_image_masks = numpy.zeros(shape=maximum_intensity_projection.shape)
for pixel_mask in plane_segmentation["pixel_mask"][:]:
for x, y, w in pixel_mask:
combined_image_masks[x,y] += w
Let's plot the image mask aggregated over all the ROIs.
Brighter values indicate overlap across multiple ROIs (since all of our weights are
Brighter values indicate overlap across multiple ROIs (since all of our weights are
1.0).
In [31]:
Copied!
plot_image(image=combined_image_masks)
plot_image(image=combined_image_masks)
3.3 Segmented and Processed Traces¶
There are a number of accessible traces in the NWB file, including raw fluorescence, neuropil corrected traces, demixed traces, and $\Delta F / F$ traces.
These are split across two containers; the DfOverF (containing only a single calculated trace) and Fluorescence from which it was calculated (containing three individual steps of the processing pipeline).
Be sure to read the description of each object to understand how they relate to each other! The following image from the Allen Institute does a good job explaining the workflow.
In [32]:
Copied!
nwbfile.processing["ophys"]["DfOverF"]
nwbfile.processing["ophys"]["DfOverF"]
Out[32]:
DfOverF
roi_response_series
DfOverF
resolution: -1.0
comments: no comments
description: The normalized ΔF/F trace calculated using the AllenSDK. Please consult the AllenSDK for details of the calculation.
conversion: 1.0
offset: 0.0
unit: a.u.
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
timestamps_unit: seconds
interval: 1
rois
description: The regions of interest (ROIs) this response series refers to.
table
description: Segmented regions of interest (ROI).
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
DfOverFEvents
resolution: -1.0
comments: no comments
description: Events from the ΔF/F detected using the L0 method from the AllenSDK. Please consult the AllenSDK for more details of the calculation.
conversion: 1.0
offset: 0.0
unit: a.u.
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
timestamps_unit: seconds
interval: 1
rois
description: The regions of interest (ROIs) this response series refers to.
table
description: Segmented regions of interest (ROI).
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
In [33]:
Copied!
nwbfile.processing["ophys"]["Fluorescence"]
nwbfile.processing["ophys"]["Fluorescence"]
Out[33]:
Fluorescence
roi_response_series
Corrected
resolution: -1.0
comments: no comments
description: Fluorescence per region of interest (ROI) from the raw imaging after spatial demixing and subtraction of neuropil background, but prior to dF/F normalization.
conversion: 1.0
offset: 0.0
unit: n.a.
data
timestamps
timestamps_unit: seconds
interval: 1
rois
description: The regions of interest (ROIs) this response series refers to.
table
description: Segmented regions of interest (ROI).
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
timestamp_link
0: processing/ophys/DfOverF/DfOverF/timestamps
1: processing/ophys/DfOverF/DfOverFEvents/timestamps
2: processing/ophys/Fluorescence/Demixed/timestamps
3: processing/ophys/Fluorescence/Neuropil/timestamps
Demixed
resolution: -1.0
comments: no comments
description: Spatially demixed traces of potentially overlapping masks.
conversion: 1.0
offset: 0.0
unit: n.a.
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
timestamps_unit: seconds
interval: 1
rois
description: The regions of interest (ROIs) this response series refers to.
table
description: Segmented regions of interest (ROI).
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
Neuropil
resolution: -1.0
comments: no comments
description: Fluorescence contaminated by background neuropil.
conversion: 1.0
offset: 0.0
unit: n.a.
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
timestamps_unit: seconds
interval: 1
rois
description: The regions of interest (ROIs) this response series refers to.
table
description: Segmented regions of interest (ROI).
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
Let's examine the $\Delta F / F$ activity of the first ROI.
In [34]:
Copied!
df_over_f = nwbfile.processing["ophys"]["DfOverF"]["DfOverF"].data
df_over_f_timestamps = nwbfile.processing["ophys"]["DfOverF"]["DfOverF"].timestamps[...]
df_over_f = nwbfile.processing["ophys"]["DfOverF"]["DfOverF"].data
df_over_f_timestamps = nwbfile.processing["ophys"]["DfOverF"]["DfOverF"].timestamps[...]
By setting our variable
df_over_f to the .data, but not slicing it like we did .timestamps[:], we are delaying the downloading of any data until we determine which ROI or frame indices we want to select in the cell below.
In [35]:
Copied!
roi_index = 0
plot_traces(timestamps=df_over_f_timestamps, traces=df_over_f[:, roi_index], y_axis_label = "$\Delta F \ / \ F$ (a.u.)")
roi_index = 0
plot_traces(timestamps=df_over_f_timestamps, traces=df_over_f[:, roi_index], y_axis_label = "$\Delta F \ / \ F$ (a.u.)")
We can examine a narrower time window by slicing the frames (the first axis of the data) - but we have to take care to apply the same frame selection to both the data and the timestamps.
Let's start by seeing how many frames we have in total, then choose a subselection.
In [36]:
Copied!
df_over_f_timestamps.shape
df_over_f_timestamps.shape
Out[36]:
(113888,)
In [37]:
Copied!
frame_slice = slice(10_000, 30_000)
plot_traces(timestamps=df_over_f_timestamps[frame_slice], traces=df_over_f[frame_slice, roi_index], y_axis_label = "$\Delta F \ / \ F$ (a.u.)")
frame_slice = slice(10_000, 30_000)
plot_traces(timestamps=df_over_f_timestamps[frame_slice], traces=df_over_f[frame_slice, roi_index], y_axis_label = "$\Delta F \ / \ F$ (a.u.)")
Now, let's look at the first 50 ROIs.
In [38]:
Copied!
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 0:50],
figure_shape=(10, 8),
y_axis_label = "$\Delta F \ / \ F$ (a.u.)",
trace_color="gray",
)
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 0:50],
figure_shape=(10, 8),
y_axis_label = "$\Delta F \ / \ F$ (a.u.)",
trace_color="gray",
)
It looks like different cells are active at different times. What could that be about?
3.4 Stimulus epochs¶
Several stimuli are shown during each imaging session, interleaved with each other. The stimulus epoch table provides information of these interleaved stimulus epochs.
Beyond this point, we assume you are using
session_id="501559087" or similar. Most but not all of the sessions in the dataset have an epochs table, so if you have tried to use a different session ID and find it does not have epochs table, please try another session.
In [39]:
Copied!
epoch_table = nwbfile.epochs
epoch_table
epoch_table = nwbfile.epochs
epoch_table
Out[39]:
epochs (TimeIntervals)
description: Coarse grain experiment structure in the alternating presentations of visual stimuli.
table
| start_time | stop_time | stimulus_type | |
|---|---|---|---|
| id | |||
| 0 | 51.55381 | 531.93378 | static_gratings |
| 1 | 561.98893 | 1042.44259 | natural_scenes |
| 2 | 1047.42969 | 1342.66703 | spontaneous |
| 3 | 1342.70028 | 1823.72944 | natural_scenes |
... and 4 more rows.
In [40]:
Copied!
epoch_table.stimulus_type.data[:]
epoch_table.stimulus_type.data[:]
Out[40]:
array(['static_gratings', 'natural_scenes', 'spontaneous',
'natural_scenes', 'static_gratings', 'natural_movie_one',
'natural_scenes', 'static_gratings'], dtype=object)
In [41]:
Copied!
frame = plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 0:50],
figure_shape=(10, 8),
y_axis_label = "$\Delta F \ / \ F$ (a.u.)",
trace_color="gray",
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
frame = plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 0:50],
figure_shape=(10, 8),
y_axis_label = "$\Delta F \ / \ F$ (a.u.)",
trace_color="gray",
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
3.5 Running speed¶
This is the running speed of the animal on the rotating disk throughout the entire session.
In [42]:
Copied!
running_speed = nwbfile.processing["behavior"]["BehavioralTimeSeries"]["running_speed"]
running_speed
running_speed = nwbfile.processing["behavior"]["BehavioralTimeSeries"]["running_speed"]
running_speed
Out[42]:
running_speed (TimeSeries)
resolution: -1.0
comments: no comments
description: Velocity of the subject over time. Mice were positioned on a running disk during the imaging sessions, and a magnetic shaft encoder (US Digital) attached to this disk recorded the running speed of the mouse during the experiment at 60 samples per second. The running speed was down-sampled to match the timing of the 2-photon imaging (30 Hz).
conversion: 1.0
offset: 0.0
unit: cm/s
data
timestamps
timestamps_unit: seconds
interval: 1
In [43]:
Copied!
plot_traces(timestamps=running_speed.timestamps[:], traces=running_speed.data[:], hide_y_scale=False, y_axis_label="Running speed (cm/s)")
plot_traces(timestamps=running_speed.timestamps[:], traces=running_speed.data[:], hide_y_scale=False, y_axis_label="Running speed (cm/s)")
Now let's combine this with the plots from sections 3.3 & 3.4!
In [44]:
Copied!
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 0:50],
figure_shape=(14,10),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 0:50],
figure_shape=(14,10),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
Interesting things¶
There are some interesting neurons here!
Beyond this point, we assume you are using
While you should probably do this your first time running the notebook - once you are familiar, you should see if the other 1517 sessions can reproduce the same results!
session_id="501559087" to exactly reproduce the plots as shown on GitHub.
While you should probably do this your first time running the notebook - once you are familiar, you should see if the other 1517 sessions can reproduce the same results!
In [45]:
Copied!
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 49],
figure_shape=(14,4),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 49],
figure_shape=(14,4),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
In [46]:
Copied!
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 4],
figure_shape=(14,4),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 4],
figure_shape=(14,4),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
In [47]:
Copied!
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 35],
figure_shape=(14,4),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
plot_traces(
timestamps=df_over_f_timestamps,
traces=df_over_f[:, 35],
figure_shape=(14,4),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
3.6 Extracted events from $\Delta F / F$¶
Sean Jewell and Daniella Witten developed an L0 method to extract to events from the $\Delta F / F$ traces.
Beyond this point, we assume you are using
session_id="501559087" or similar. Most but not all of the sessions in the dataset have identified DFOverFEvents events, so if you have tried to use a different session ID and find it does not have this object, please try another session.
In [48]:
Copied!
df_over_f_events = nwbfile.processing["ophys"]["DfOverF"]["DfOverFEvents"]
df_over_f_events
df_over_f_events = nwbfile.processing["ophys"]["DfOverF"]["DfOverFEvents"]
df_over_f_events
Out[48]:
DfOverFEvents (RoiResponseSeries)
resolution: -1.0
comments: no comments
description: Events from the ΔF/F detected using the L0 method from the AllenSDK. Please consult the AllenSDK for more details of the calculation.
conversion: 1.0
offset: 0.0
unit: a.u.
data
timestamps (link to processing/ophys/Fluorescence/Corrected/timestamps)
timestamps_unit: seconds
interval: 1
rois
description: The regions of interest (ROIs) this response series refers to.
table
description: Segmented regions of interest (ROI).
imaging_plane
optical_channel
0
description: An optical channel used to collection light emission during two-photon calcium imaging.
emission_lambda: nan
description: The imaging plane sampled by the two-photon calcium imaging at a depth of {depth} µm.
device
description: A Nikon A1R-MP multiphoton microscope. This system was adapted to provide space to accommodate the behavior apparatus.
manufacturer: Nikon
excitation_lambda: 910.0
indicator: GCaMP6f
location: VISp
conversion: 1.0
unit: meters
origin_coords_unit: meters
grid_spacing
grid_spacing_unit: micrometers
table
| global_roi_id | pixel_mask | |
|---|---|---|
| id | ||
| 517473350 | 517473350 | [[55, 291, 1.0], [55, 292, 1.0], [56, 290, 1.0], [56, 291, 1.0], [56, 292, 1.0], [56, 293, 1.0], [56, 294, 1.0], [56, 295, 1.0], [56, 296, 1.0], [57, 289, 1.0], [57, 290, 1.0], [57, 291, 1.0], [57, 292, 1.0], [57, 293, 1.0], [57, 294, 1.0], [57, 295, 1.0], [57, 296, 1.0], [57, 297, 1.0], [57, 298, 1.0], [58, 288, 1.0], [58, 289, 1.0], [58, 290, 1.0], [58, 291, 1.0], [58, 292, 1.0], [58, 293, 1.0], [58, 294, 1.0], [58, 295, 1.0], [58, 296, 1.0], [58, 297, 1.0], [58, 298, 1.0], [58, 299, 1.0], [59, 288, 1.0], [59, 289, 1.0], [59, 290, 1.0], [59, 291, 1.0], [59, 292, 1.0], [59, 293, 1.0], [59, 294, 1.0], [59, 295, 1.0], [59, 296, 1.0], [59, 297, 1.0], [59, 298, 1.0], [59, 299, 1.0], [60, 288, 1.0], [60, 289, 1.0], [60, 290, 1.0], [60, 291, 1.0], [60, 292, 1.0], [60, 293, 1.0], [60, 294, 1.0], [60, 295, 1.0], [60, 296, 1.0], [60, 297, 1.0], [60, 298, 1.0], [60, 299, 1.0], [61, 287, 1.0], [61, 288, 1.0], [61, 289, 1.0], [61, 290, 1.0], [61, 291, 1.0], [61, 292, 1.0], [61, 293, 1.0], [61, 294, 1.0], [61, 295, 1.0], [61, 296, 1.0], [61, 297, 1.0], [61, 298, 1.0], [61, 299, 1.0], [62, 287, 1.0], [62, 288, 1.0], [62, 289, 1.0], [62, 290, 1.0], [62, 291, 1.0], [62, 292, 1.0], [62, 293, 1.0], [62, 294, 1.0], [62, 295, 1.0], [62, 296, 1.0], [62, 297, 1.0], [62, 298, 1.0], [62, 299, 1.0], [63, 287, 1.0], [63, 288, 1.0], [63, 289, 1.0], [63, 290, 1.0], [63, 291, 1.0], [63, 292, 1.0], [63, 293, 1.0], [63, 294, 1.0], [63, 295, 1.0], [63, 296, 1.0], [63, 297, 1.0], [63, 298, 1.0], [63, 299, 1.0], [64, 287, 1.0], [64, 288, 1.0], [64, 289, 1.0], [64, 290, 1.0], [64, 291, 1.0], [64, 292, 1.0], ...] |
| 517473341 | 517473341 | [[58, 272, 1.0], [58, 273, 1.0], [58, 274, 1.0], [58, 275, 1.0], [58, 276, 1.0], [58, 277, 1.0], [59, 271, 1.0], [59, 272, 1.0], [59, 273, 1.0], [59, 274, 1.0], [59, 275, 1.0], [59, 276, 1.0], [59, 277, 1.0], [59, 278, 1.0], [59, 279, 1.0], [59, 280, 1.0], [59, 281, 1.0], [59, 282, 1.0], [60, 271, 1.0], [60, 272, 1.0], [60, 273, 1.0], [60, 274, 1.0], [60, 275, 1.0], [60, 276, 1.0], [60, 277, 1.0], [60, 278, 1.0], [60, 279, 1.0], [60, 280, 1.0], [60, 281, 1.0], [60, 282, 1.0], [60, 283, 1.0], [61, 270, 1.0], [61, 271, 1.0], [61, 272, 1.0], [61, 273, 1.0], [61, 274, 1.0], [61, 275, 1.0], [61, 276, 1.0], [61, 277, 1.0], [61, 278, 1.0], [61, 279, 1.0], [61, 280, 1.0], [61, 281, 1.0], [61, 282, 1.0], [62, 270, 1.0], [62, 271, 1.0], [62, 272, 1.0], [62, 273, 1.0], [62, 274, 1.0], [62, 275, 1.0], [62, 276, 1.0], [62, 277, 1.0], [62, 278, 1.0], [62, 279, 1.0], [62, 280, 1.0], [62, 281, 1.0], [62, 282, 1.0], [63, 269, 1.0], [63, 270, 1.0], [63, 271, 1.0], [63, 272, 1.0], [63, 273, 1.0], [63, 274, 1.0], [63, 275, 1.0], [63, 276, 1.0], [63, 277, 1.0], [63, 278, 1.0], [63, 279, 1.0], [63, 280, 1.0], [63, 281, 1.0], [63, 282, 1.0], [64, 268, 1.0], [64, 269, 1.0], [64, 270, 1.0], [64, 271, 1.0], [64, 272, 1.0], [64, 273, 1.0], [64, 274, 1.0], [64, 275, 1.0], [64, 276, 1.0], [64, 277, 1.0], [64, 278, 1.0], [64, 279, 1.0], [64, 280, 1.0], [64, 281, 1.0], [64, 282, 1.0], [65, 268, 1.0], [65, 269, 1.0], [65, 270, 1.0], [65, 271, 1.0], [65, 272, 1.0], [65, 273, 1.0], [65, 274, 1.0], [65, 275, 1.0], [65, 276, 1.0], [65, 277, 1.0], [65, 278, 1.0], [65, 279, 1.0], [65, 280, 1.0], [65, 281, 1.0], ...] |
| 517473313 | 517473313 | [[33, 459, 1.0], [33, 460, 1.0], [33, 461, 1.0], [33, 462, 1.0], [33, 463, 1.0], [34, 458, 1.0], [34, 459, 1.0], [34, 460, 1.0], [34, 461, 1.0], [34, 462, 1.0], [34, 463, 1.0], [34, 464, 1.0], [35, 457, 1.0], [35, 458, 1.0], [35, 459, 1.0], [35, 460, 1.0], [35, 461, 1.0], [35, 462, 1.0], [35, 463, 1.0], [35, 464, 1.0], [35, 465, 1.0], [35, 466, 1.0], [36, 456, 1.0], [36, 457, 1.0], [36, 458, 1.0], [36, 459, 1.0], [36, 460, 1.0], [36, 461, 1.0], [36, 462, 1.0], [36, 463, 1.0], [36, 464, 1.0], [36, 465, 1.0], [36, 466, 1.0], [36, 467, 1.0], [37, 456, 1.0], [37, 457, 1.0], [37, 458, 1.0], [37, 459, 1.0], [37, 460, 1.0], [37, 461, 1.0], [37, 462, 1.0], [37, 463, 1.0], [37, 464, 1.0], [37, 465, 1.0], [37, 466, 1.0], [37, 467, 1.0], [38, 455, 1.0], [38, 456, 1.0], [38, 457, 1.0], [38, 458, 1.0], [38, 459, 1.0], [38, 460, 1.0], [38, 461, 1.0], [38, 462, 1.0], [38, 463, 1.0], [38, 464, 1.0], [38, 465, 1.0], [38, 466, 1.0], [38, 467, 1.0], [39, 455, 1.0], [39, 456, 1.0], [39, 457, 1.0], [39, 458, 1.0], [39, 459, 1.0], [39, 460, 1.0], [39, 461, 1.0], [39, 462, 1.0], [39, 463, 1.0], [39, 464, 1.0], [39, 465, 1.0], [39, 466, 1.0], [39, 467, 1.0], [40, 455, 1.0], [40, 456, 1.0], [40, 457, 1.0], [40, 458, 1.0], [40, 459, 1.0], [40, 460, 1.0], [40, 461, 1.0], [40, 462, 1.0], [40, 463, 1.0], [40, 464, 1.0], [40, 465, 1.0], [40, 466, 1.0], [41, 455, 1.0], [41, 456, 1.0], [41, 457, 1.0], [41, 458, 1.0], [41, 459, 1.0], [41, 460, 1.0], [41, 461, 1.0], [41, 462, 1.0], [41, 463, 1.0], [41, 464, 1.0], [41, 465, 1.0], [41, 466, 1.0], [42, 455, 1.0], [42, 456, 1.0], [42, 457, 1.0], [42, 458, 1.0], ...] |
| 517473255 | 517473255 | [[41, 493, 1.0], [41, 494, 1.0], [41, 495, 1.0], [41, 496, 1.0], [42, 492, 1.0], [42, 493, 1.0], [42, 494, 1.0], [42, 495, 1.0], [42, 496, 1.0], [42, 497, 1.0], [43, 489, 1.0], [43, 490, 1.0], [43, 491, 1.0], [43, 492, 1.0], [43, 493, 1.0], [43, 494, 1.0], [43, 495, 1.0], [43, 496, 1.0], [43, 497, 1.0], [43, 498, 1.0], [44, 488, 1.0], [44, 489, 1.0], [44, 490, 1.0], [44, 491, 1.0], [44, 492, 1.0], [44, 493, 1.0], [44, 494, 1.0], [44, 495, 1.0], [44, 496, 1.0], [44, 497, 1.0], [44, 498, 1.0], [45, 487, 1.0], [45, 488, 1.0], [45, 489, 1.0], [45, 490, 1.0], [45, 491, 1.0], [45, 492, 1.0], [45, 493, 1.0], [45, 494, 1.0], [45, 495, 1.0], [45, 496, 1.0], [45, 497, 1.0], [45, 498, 1.0], [46, 486, 1.0], [46, 487, 1.0], [46, 488, 1.0], [46, 489, 1.0], [46, 490, 1.0], [46, 491, 1.0], [46, 492, 1.0], [46, 493, 1.0], [46, 494, 1.0], [46, 495, 1.0], [46, 496, 1.0], [46, 497, 1.0], [46, 498, 1.0], [47, 486, 1.0], [47, 487, 1.0], [47, 488, 1.0], [47, 489, 1.0], [47, 490, 1.0], [47, 491, 1.0], [47, 492, 1.0], [47, 493, 1.0], [47, 494, 1.0], [47, 495, 1.0], [47, 496, 1.0], [47, 497, 1.0], [47, 498, 1.0], [47, 499, 1.0], [48, 486, 1.0], [48, 487, 1.0], [48, 488, 1.0], [48, 489, 1.0], [48, 490, 1.0], [48, 491, 1.0], [48, 492, 1.0], [48, 493, 1.0], [48, 494, 1.0], [48, 495, 1.0], [48, 496, 1.0], [48, 497, 1.0], [48, 498, 1.0], [48, 499, 1.0], [49, 486, 1.0], [49, 487, 1.0], [49, 488, 1.0], [49, 489, 1.0], [49, 490, 1.0], [49, 491, 1.0], [49, 492, 1.0], [49, 493, 1.0], [49, 494, 1.0], [49, 495, 1.0], [49, 496, 1.0], [49, 497, 1.0], [49, 498, 1.0], [49, 499, 1.0], [50, 486, 1.0], [50, 487, 1.0], ...] |
... and 170 more rows.
Let's plot both the events and the $\Delta F / F$ trace for one neuron (say neuron index #1).
In [49]:
Copied!
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=2 * df_over_f_events.data[:, 1], # Arbitrarily scale by 2 for better appearance
)
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=df_over_f[:, 1] - 5, # Arbitrarily shift by 5 for better appearance
reset_figure=False,
)
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=2 * df_over_f_events.data[:, 1], # Arbitrarily scale by 2 for better appearance
)
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=df_over_f[:, 1] - 5, # Arbitrarily shift by 5 for better appearance
reset_figure=False,
)
Now let's zoom in to a relevant portion.
In [50]:
Copied!
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=2 * df_over_f_events.data[:, 1], # Arbitrarily scale by 2 for better appearance
)
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=df_over_f[:, 1] - 5, # Arbitrarily shift by 5 for better appearance
reset_figure=False,
)
matplotlib_pyplot.xlim(690, 720); # in units seconds
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=2 * df_over_f_events.data[:, 1], # Arbitrarily scale by 2 for better appearance
)
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=df_over_f[:, 1] - 5, # Arbitrarily shift by 5 for better appearance
reset_figure=False,
)
matplotlib_pyplot.xlim(690, 720); # in units seconds
Now let's remake our plot of neural activity, stimulus, and running, but using the extracted events instead of $\Delta F / F$.
In [51]:
Copied!
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=df_over_f_events.data[:, 0:50],
figure_shape=(14,10),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
plot_traces(
timestamps=df_over_f_events.timestamps[:],
traces=df_over_f_events.data[:, 0:50],
figure_shape=(14,10),
trace_color="gray",
)
frame = plot_traces(
timestamps=running_speed.timestamps[:],
traces=(0.2 * running_speed.data[:]) - 20, # Apply some custom scaling to get both traces on the same x-axis
reset_figure=False,
return_frame=True,
)
apply_epoch_shading(frame=frame, epoch_table=epoch_table)
3.7 Stimulus Module¶
For each stimulus there is a neurodata object with information about the condition and timing of each trial.
3.7a Natural Scenes¶
For the natural scenes, these are IndexSeries whose data are the indices of the corresponding stimulus template and whose timestamps are the time in seconds when each template was presented.
Some sessions may also have 'locally sparse' noise of varying degrees, which can be accessed in an identical fashion.
In [52]:
Copied!
natural_scene_presentation = nwbfile.stimulus["natural_scenes_stimulus"]
natural_scene_presentation
natural_scene_presentation = nwbfile.stimulus["natural_scenes_stimulus"]
natural_scene_presentation
Out[52]:
natural_scenes_stimulus (IndexSeries)
resolution: -1.0
comments: no comments
description: The order and timing for presentation of the natural scene templates.
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
In [53]:
Copied!
natural_scene_presentation = nwbfile.stimulus["natural_scenes_stimulus"]
natural_scene_presentation.data[:]
natural_scene_presentation = nwbfile.stimulus["natural_scenes_stimulus"]
natural_scene_presentation.data[:]
Out[53]:
array([81, 33, 76, ..., 34, 0, 87], dtype=uint32)
In [54]:
Copied!
natural_scene_presentation.timestamps[:]
natural_scene_presentation.timestamps[:]
Out[54]:
array([ 561.98893, 562.25491, 562.48764, ..., 3221.9665 , 3222.23249,
3222.46524])
The actual images and movies presented during the session area also included in the NWB file as the stimulus template.
Stimuli that are generated programmatically (e.g., drifting and static gratings) do not have a stimulus template.
In [55]:
Copied!
nwbfile.stimulus_template["natural_scenes_template"]
nwbfile.stimulus_template["natural_scenes_template"]
Out[55]:
natural_scenes_template (Images)
description: A collection of natural scenes presented to the subject. Lasted for exactly 7 frames.
images
NaturalScene0
NaturalScene1
NaturalScene10
NaturalScene100
NaturalScene101
NaturalScene102
NaturalScene103
NaturalScene104
NaturalScene105
NaturalScene106
NaturalScene107
NaturalScene108
NaturalScene109
NaturalScene11
NaturalScene110
NaturalScene111
NaturalScene112
NaturalScene113
NaturalScene114
NaturalScene115
NaturalScene116
NaturalScene117
NaturalScene12
NaturalScene13
NaturalScene14
NaturalScene15
NaturalScene16
NaturalScene17
NaturalScene18
NaturalScene19
NaturalScene2
NaturalScene20
NaturalScene21
NaturalScene22
NaturalScene23
NaturalScene24
NaturalScene25
NaturalScene26
NaturalScene27
NaturalScene28
NaturalScene29
NaturalScene3
NaturalScene30
NaturalScene31
NaturalScene32
NaturalScene33
NaturalScene34
NaturalScene35
NaturalScene36
NaturalScene37
NaturalScene38
NaturalScene39
NaturalScene4
NaturalScene40
NaturalScene41
NaturalScene42
NaturalScene43
NaturalScene44
NaturalScene45
NaturalScene46
NaturalScene47
NaturalScene48
NaturalScene49
NaturalScene5
NaturalScene50
NaturalScene51
NaturalScene52
NaturalScene53
NaturalScene54
NaturalScene55
NaturalScene56
NaturalScene57
NaturalScene58
NaturalScene59
NaturalScene6
NaturalScene60
NaturalScene61
NaturalScene62
NaturalScene63
NaturalScene64
NaturalScene65
NaturalScene66
NaturalScene67
NaturalScene68
NaturalScene69
NaturalScene7
NaturalScene70
NaturalScene71
NaturalScene72
NaturalScene73
NaturalScene74
NaturalScene75
NaturalScene76
NaturalScene77
NaturalScene78
NaturalScene79
NaturalScene8
NaturalScene80
NaturalScene81
NaturalScene82
NaturalScene83
NaturalScene84
NaturalScene85
NaturalScene86
NaturalScene87
NaturalScene88
NaturalScene89
NaturalScene9
NaturalScene90
NaturalScene91
NaturalScene92
NaturalScene93
NaturalScene94
NaturalScene95
NaturalScene96
NaturalScene97
NaturalScene98
NaturalScene99
In [56]:
Copied!
natural_scene_template = nwbfile.stimulus_template["natural_scenes_template"]
natural_scene_template
natural_scene_template = nwbfile.stimulus_template["natural_scenes_template"]
natural_scene_template
Out[56]:
natural_scenes_template (Images)
description: A collection of natural scenes presented to the subject. Lasted for exactly 7 frames.
images
NaturalScene0
NaturalScene1
NaturalScene10
NaturalScene100
NaturalScene101
NaturalScene102
NaturalScene103
NaturalScene104
NaturalScene105
NaturalScene106
NaturalScene107
NaturalScene108
NaturalScene109
NaturalScene11
NaturalScene110
NaturalScene111
NaturalScene112
NaturalScene113
NaturalScene114
NaturalScene115
NaturalScene116
NaturalScene117
NaturalScene12
NaturalScene13
NaturalScene14
NaturalScene15
NaturalScene16
NaturalScene17
NaturalScene18
NaturalScene19
NaturalScene2
NaturalScene20
NaturalScene21
NaturalScene22
NaturalScene23
NaturalScene24
NaturalScene25
NaturalScene26
NaturalScene27
NaturalScene28
NaturalScene29
NaturalScene3
NaturalScene30
NaturalScene31
NaturalScene32
NaturalScene33
NaturalScene34
NaturalScene35
NaturalScene36
NaturalScene37
NaturalScene38
NaturalScene39
NaturalScene4
NaturalScene40
NaturalScene41
NaturalScene42
NaturalScene43
NaturalScene44
NaturalScene45
NaturalScene46
NaturalScene47
NaturalScene48
NaturalScene49
NaturalScene5
NaturalScene50
NaturalScene51
NaturalScene52
NaturalScene53
NaturalScene54
NaturalScene55
NaturalScene56
NaturalScene57
NaturalScene58
NaturalScene59
NaturalScene6
NaturalScene60
NaturalScene61
NaturalScene62
NaturalScene63
NaturalScene64
NaturalScene65
NaturalScene66
NaturalScene67
NaturalScene68
NaturalScene69
NaturalScene7
NaturalScene70
NaturalScene71
NaturalScene72
NaturalScene73
NaturalScene74
NaturalScene75
NaturalScene76
NaturalScene77
NaturalScene78
NaturalScene79
NaturalScene8
NaturalScene80
NaturalScene81
NaturalScene82
NaturalScene83
NaturalScene84
NaturalScene85
NaturalScene86
NaturalScene87
NaturalScene88
NaturalScene89
NaturalScene9
NaturalScene90
NaturalScene91
NaturalScene92
NaturalScene93
NaturalScene94
NaturalScene95
NaturalScene96
NaturalScene97
NaturalScene98
NaturalScene99
In [57]:
Copied!
len(natural_scene_template.images)
len(natural_scene_template.images)
Out[57]:
118
Let's plot the natural scene image indexed at #101.
In [58]:
Copied!
scene_number = 101
plot_image(image=natural_scene_template[f"NaturalScene{scene_number}"], color_map="gray")
scene_number = 101
plot_image(image=natural_scene_template[f"NaturalScene{scene_number}"], color_map="gray")
Use the stimulus table to plot the responses of cell index 1 to image 101
3.7b Natural Movies¶
The natural movies are very similar to the natural scenes, except that their data are the indices to a single ImageSeries (instead of multiple Image objects stored in an Image container) found in the templates.
In [59]:
Copied!
natural_movie_one_presentation = nwbfile.stimulus["natural_movie_one_stimulus"]
natural_movie_one_presentation
natural_movie_one_presentation = nwbfile.stimulus["natural_movie_one_stimulus"]
natural_movie_one_presentation
Out[59]:
natural_movie_one_stimulus (IndexSeries)
resolution: -1.0
comments: no comments
description: The order and timing for presentation of frames from the the natural movie templates.
conversion: 1.0
offset: 0.0
unit: N/A
data
timestamps
timestamps_unit: seconds
interval: 1
indexed_timeseries
starting_time: nan
rate: nan
resolution: -1.0
comments: no comments
description: A natural movie presented to the subject.
conversion: 1.0
offset: 0.0
unit: n.a.
data
starting_time_unit: seconds
device
description: An ASUS PA248Q monitor used to display visual stimuli.
manufacturer: ASUS
In [60]:
Copied!
natural_movie_one_template = nwbfile.stimulus_template["natural_movie_one"]
natural_movie_one_template
natural_movie_one_template = nwbfile.stimulus_template["natural_movie_one"]
natural_movie_one_template
Out[60]:
natural_movie_one (ImageSeries)
starting_time: nan
rate: nan
resolution: -1.0
comments: no comments
description: A natural movie presented to the subject.
conversion: 1.0
offset: 0.0
unit: n.a.
data
starting_time_unit: seconds
device
description: An ASUS PA248Q monitor used to display visual stimuli.
manufacturer: ASUS
In [61]:
Copied!
natural_movie_one_template.data.shape
natural_movie_one_template.data.shape
Out[61]:
(900, 304, 608)
Let's plot the image of the frame index #101 from the natural movie.
In [62]:
Copied!
frame_index = 101
plot_image(image=natural_movie_one_template.data[frame_index, ...], color_map="gray")
frame_index = 101
plot_image(image=natural_movie_one_template.data[frame_index, ...], color_map="gray")
3.7c Static Gratings¶
For the static gratings, these are simple a table with the corresponding parameters that were varied on each presentation.
In [63]:
Copied!
static_gratings_table = nwbfile.stimulus["static_gratings"]
static_gratings_table.to_dataframe()
static_gratings_table = nwbfile.stimulus["static_gratings"]
static_gratings_table.to_dataframe()
Out[63]:
| start_time | stop_time | orientation_in_degrees | spatial_frequency_in_cycles_per_degree | phase | is_blank_sweep | |
|---|---|---|---|---|---|---|
| id | ||||||
| 0 | 51.55381 | 51.80381 | 90.0 | 0.04 | 0.5 | False |
| 1 | 51.78653 | 52.03653 | 150.0 | 0.04 | 0.5 | False |
| 2 | 52.05250 | 52.30250 | 30.0 | 0.02 | 0.0 | False |
What does each of these parameters mean?
You can always access descriptions of such columns if you're wondering about what each one means in the context of the experiment.
You can always access descriptions of such columns if you're wondering about what each one means in the context of the experiment.
In [64]:
Copied!
static_gratings_column_descriptions = {column.name: column.description for column in static_gratings_table.columns}
static_gratings_column_descriptions
static_gratings_column_descriptions = {column.name: column.description for column in static_gratings_table.columns}
static_gratings_column_descriptions
Out[64]:
{'start_time': 'Start time of epoch, in seconds',
'stop_time': 'Stop time of epoch, in seconds',
'orientation_in_degrees': 'Angle of the grating in degrees. NaN values correspond to a blank sweep.',
'spatial_frequency_in_cycles_per_degree': 'Period of the grating in cycles/degree. NaN values correspond to a blank sweep.',
'phase': 'Relative position of the grating. Phase 0 and Phase 0.5 are 180° apart so that the peak of the grating of phase 0 lines up with the trough of phase 0.5. NaN values correspond to a blank sweep.',
'is_blank_sweep': 'Mean luminance gray image.'}
3.7 Drifting Gratings¶
To examine the drifting gratings, we will have to open a stream to a separate session file.
The drifting gratings are also a table with the corresponding parameters that were varied on each presentation.
In [65]:
Copied!
session_id_with_drifting_gratings = "501940850"
nwbfile_with_drifting_gratings = read_visual_coding_ophys_file(dandiset=dandiset, session_id=session_id_with_drifting_gratings)
session_id_with_drifting_gratings = "501940850"
nwbfile_with_drifting_gratings = read_visual_coding_ophys_file(dandiset=dandiset, session_id=session_id_with_drifting_gratings)
In [66]:
Copied!
drifting_gratings = nwbfile_with_drifting_gratings.stimulus["drifting_gratings"]
drifting_gratings
drifting_gratings = nwbfile_with_drifting_gratings.stimulus["drifting_gratings"]
drifting_gratings
Out[66]:
drifting_gratings (TimeIntervals)
description: Parameterizations of full field drifting sinusoidal grating at a single spatial contrast (80%). Direction of motion is to the orientation of the grating.
table
| start_time | stop_time | orientation_in_degrees | spatial_frequency_in_cycles_per_degree | temporal_frequency_in_hz | is_blank_sweep | |
|---|---|---|---|---|---|---|
| id | ||||||
| 0 | 34.35150 | 36.35150 | 135.0 | 0.04 | 1.0 | False |
| 1 | 37.33713 | 39.33713 | 0.0 | 0.04 | 2.0 | False |
| 2 | 40.35595 | 42.35595 | 0.0 | 0.04 | 8.0 | False |
| 3 | 43.34159 | 45.34159 | 225.0 | 0.04 | 8.0 | False |
... and 624 more rows.
In [67]:
Copied!
drifting_gratings_column_descriptions = {column.name: column.description for column in drifting_gratings.columns}
drifting_gratings_column_descriptions
drifting_gratings_column_descriptions = {column.name: column.description for column in drifting_gratings.columns}
drifting_gratings_column_descriptions
Out[67]:
{'start_time': 'Start time of epoch, in seconds',
'stop_time': 'Stop time of epoch, in seconds',
'orientation_in_degrees': 'Angle of the grating in degrees. NaN values correspond to a blank sweep.',
'spatial_frequency_in_cycles_per_degree': 'Period of the grating in cycles/degree. NaN values correspond to a blank sweep.',
'temporal_frequency_in_hz': 'The speed at which the grating moves in Hz. NaN values correspond to a blank sweep.',
'is_blank_sweep': 'Mean luminance gray image.'}
Part 4: Compute activity aligned to stimulus presentations
Remember earlier when we mentioned not slicing the data object meant we kept it 'lazy'? This can be really convenient for very large datasets where loading too much into memory at a time could overload our system.
But it can also decrease the speed of repeated operations - so we'll go ahead and load all the data using
But it can also decrease the speed of repeated operations - so we'll go ahead and load all the data using
[...] for these next steps!
In [68]:
Copied!
df_over_f = df_over_f[...]
df_over_f = df_over_f[...]
In [69]:
Copied!
scene_number = 101
cell_index = 1
times_when_scene_was_presented = natural_scene_presentation.timestamps[natural_scene_presentation.data[:] == scene_number]
# Applying 30 frames of buffer room before and 50 after
df_over_f_times_to_frames = numpy.searchsorted(df_over_f_timestamps, times_when_scene_was_presented)
for df_over_f_time_to_frame in df_over_f_times_to_frames[:]:
subset_df_over_f_frames = slice(df_over_f_time_to_frame - 30, df_over_f_time_to_frame + 50)
timestamps = df_over_f_timestamps[subset_df_over_f_frames]
aligned_timestamps = timestamps - timestamps[30]
frame = plot_traces(
timestamps=aligned_timestamps,
traces=df_over_f[subset_df_over_f_frames, cell_index],
x_axis_label="Time aligned to stimulus onset (s)",
y_axis_label="$\Delta F \ / \ F$ (a.u.)",
reset_figure=False,
return_frame=True,
)
# Here, we use the fact about duration of the stimulusu learned from the description of the NaturalScene objects
frame.axvspan(aligned_timestamps[30], aligned_timestamps[37], color="gray", alpha=0.3);
scene_number = 101
cell_index = 1
times_when_scene_was_presented = natural_scene_presentation.timestamps[natural_scene_presentation.data[:] == scene_number]
# Applying 30 frames of buffer room before and 50 after
df_over_f_times_to_frames = numpy.searchsorted(df_over_f_timestamps, times_when_scene_was_presented)
for df_over_f_time_to_frame in df_over_f_times_to_frames[:]:
subset_df_over_f_frames = slice(df_over_f_time_to_frame - 30, df_over_f_time_to_frame + 50)
timestamps = df_over_f_timestamps[subset_df_over_f_frames]
aligned_timestamps = timestamps - timestamps[30]
frame = plot_traces(
timestamps=aligned_timestamps,
traces=df_over_f[subset_df_over_f_frames, cell_index],
x_axis_label="Time aligned to stimulus onset (s)",
y_axis_label="$\Delta F \ / \ F$ (a.u.)",
reset_figure=False,
return_frame=True,
)
# Here, we use the fact about duration of the stimulusu learned from the description of the NaturalScene objects
frame.axvspan(aligned_timestamps[30], aligned_timestamps[37], color="gray", alpha=0.3);
In [70]:
Copied!
scene_number = 101
cell_index = 1
times_when_scene_was_presented = natural_scene_presentation.timestamps[natural_scene_presentation.data[:] == scene_number]
# Applying 30 frames of buffer room before and 50 after
df_over_f_times_to_frames = numpy.searchsorted(df_over_f_timestamps, times_when_scene_was_presented)
timestamps = df_over_f_timestamps[subset_df_over_f_frames]
aligned_timestamps = timestamps - timestamps[30]
for presentation_index, df_over_f_time_to_frame in enumerate(df_over_f_times_to_frames[:], start=1):
subset_df_over_f_frames = slice(df_over_f_time_to_frame - 30, df_over_f_time_to_frame + 50)
frame = plot_traces(
timestamps=aligned_timestamps,
traces=2 * df_over_f_events.data[subset_df_over_f_frames, cell_index] + presentation_index,
reset_figure=False,
x_axis_label="Time aligned to stimulus onset (s)",
y_axis_label="$\Delta F \ / \ F$ (a.u.)",
return_frame=True,
)
# Here, we use the fact about duration of the stimulusu learned from the description of the NaturalScene objects
frame.axvspan(aligned_timestamps[30], aligned_timestamps[37], color="gray", alpha=0.3);
scene_number = 101
cell_index = 1
times_when_scene_was_presented = natural_scene_presentation.timestamps[natural_scene_presentation.data[:] == scene_number]
# Applying 30 frames of buffer room before and 50 after
df_over_f_times_to_frames = numpy.searchsorted(df_over_f_timestamps, times_when_scene_was_presented)
timestamps = df_over_f_timestamps[subset_df_over_f_frames]
aligned_timestamps = timestamps - timestamps[30]
for presentation_index, df_over_f_time_to_frame in enumerate(df_over_f_times_to_frames[:], start=1):
subset_df_over_f_frames = slice(df_over_f_time_to_frame - 30, df_over_f_time_to_frame + 50)
frame = plot_traces(
timestamps=aligned_timestamps,
traces=2 * df_over_f_events.data[subset_df_over_f_frames, cell_index] + presentation_index,
reset_figure=False,
x_axis_label="Time aligned to stimulus onset (s)",
y_axis_label="$\Delta F \ / \ F$ (a.u.)",
return_frame=True,
)
# Here, we use the fact about duration of the stimulusu learned from the description of the NaturalScene objects
frame.axvspan(aligned_timestamps[30], aligned_timestamps[37], color="gray", alpha=0.3);
Part 5: Compute the signal correlations as a function of distance between neurons
Let's compute the mean response for each neuron in the experiment for each image of the natural scene stimulus.
In [71]:
Copied!
number_of_rois = df_over_f.shape[1]
number_of_natural_scenes = len(natural_scene_template.images)
response = numpy.empty(shape=(number_of_rois, number_of_natural_scenes))
for roi_index in range(number_of_rois):
for scene_number in range(number_of_natural_scenes):
times_when_scene_was_presented = natural_scene_presentation.timestamps[natural_scene_presentation.data[:] == scene_number]
df_over_f_times_to_frames = numpy.searchsorted(df_over_f_timestamps, times_when_scene_was_presented)
# Take the mean over a very short period just after stimulus onset (15 frames)
frames_before_onset = 0
frames_after_onset = 15
temp = numpy.empty(shape=(frames_after_onset + frames_before_onset, len(times_when_scene_was_presented)))
for temp_index, df_over_f_time_to_frame in enumerate(df_over_f_times_to_frames):
subset_df_over_f_frames = slice(df_over_f_time_to_frame - frames_before_onset, df_over_f_time_to_frame + frames_after_onset)
temp[:, temp_index] = df_over_f[subset_df_over_f_frames, roi_index]
response[roi_index, scene_number] = temp.mean()
number_of_rois = df_over_f.shape[1]
number_of_natural_scenes = len(natural_scene_template.images)
response = numpy.empty(shape=(number_of_rois, number_of_natural_scenes))
for roi_index in range(number_of_rois):
for scene_number in range(number_of_natural_scenes):
times_when_scene_was_presented = natural_scene_presentation.timestamps[natural_scene_presentation.data[:] == scene_number]
df_over_f_times_to_frames = numpy.searchsorted(df_over_f_timestamps, times_when_scene_was_presented)
# Take the mean over a very short period just after stimulus onset (15 frames)
frames_before_onset = 0
frames_after_onset = 15
temp = numpy.empty(shape=(frames_after_onset + frames_before_onset, len(times_when_scene_was_presented)))
for temp_index, df_over_f_time_to_frame in enumerate(df_over_f_times_to_frames):
subset_df_over_f_frames = slice(df_over_f_time_to_frame - frames_before_onset, df_over_f_time_to_frame + frames_after_onset)
temp[:, temp_index] = df_over_f[subset_df_over_f_frames, roi_index]
response[roi_index, scene_number] = temp.mean()
In [72]:
Copied!
matplotlib_pyplot.plot(response[0,:], 'o-');
matplotlib_pyplot.plot(response[0,:], 'o-');
Let's compute the signal correlation between all neuron pairs.
This is the correlation of the mean response to all images.
This is the correlation of the mean response to all images.
In [73]:
Copied!
signal_correlations = numpy.empty(shape=(number_of_rois, number_of_rois))
for roi_index_1 in range(number_of_rois):
for roi_index_2 in range(number_of_rois):
r, p = scipy_stats.pearsonr(response[roi_index_1, :], response[roi_index_2, :])
signal_correlations[roi_index_1, roi_index_2] = r
signal_correlations = numpy.empty(shape=(number_of_rois, number_of_rois))
for roi_index_1 in range(number_of_rois):
for roi_index_2 in range(number_of_rois):
r, p = scipy_stats.pearsonr(response[roi_index_1, :], response[roi_index_2, :])
signal_correlations[roi_index_1, roi_index_2] = r
In [74]:
Copied!
matplotlib_pyplot.imshow(signal_correlations);
matplotlib_pyplot.imshow(signal_correlations);
Let's use the ROI masks to calculate the distance between each pair of neurons.
In [75]:
Copied!
pixel_masks = plane_segmentation["pixel_mask"][:]
centroid_x = numpy.zeros(shape=number_of_rois)
centroid_y = numpy.zeros(shape=number_of_rois)
for roi_index, pixel_mask in enumerate(pixel_masks):
centroid_x[roi_index] = numpy.mean([pixel[0] for pixel in pixel_mask])
centroid_y[roi_index] = numpy.mean([pixel[1] for pixel in pixel_mask])
distance = numpy.zeros(shape=(number_of_rois, number_of_rois))
for roi_index_1 in range(number_of_rois):
for roi_index_2 in range(number_of_rois):
distance[roi_index_1, roi_index_2] = numpy.sqrt(
(centroid_x[roi_index_1] - centroid_x[roi_index_2])**2 + (centroid_y[roi_index_1] - centroid_y[roi_index_2])**2
)
pixel_masks = plane_segmentation["pixel_mask"][:]
centroid_x = numpy.zeros(shape=number_of_rois)
centroid_y = numpy.zeros(shape=number_of_rois)
for roi_index, pixel_mask in enumerate(pixel_masks):
centroid_x[roi_index] = numpy.mean([pixel[0] for pixel in pixel_mask])
centroid_y[roi_index] = numpy.mean([pixel[1] for pixel in pixel_mask])
distance = numpy.zeros(shape=(number_of_rois, number_of_rois))
for roi_index_1 in range(number_of_rois):
for roi_index_2 in range(number_of_rois):
distance[roi_index_1, roi_index_2] = numpy.sqrt(
(centroid_x[roi_index_1] - centroid_x[roi_index_2])**2 + (centroid_y[roi_index_1] - centroid_y[roi_index_2])**2
)
In [76]:
Copied!
matplotlib_pyplot.imshow(distance);
matplotlib_pyplot.imshow(distance);
Let's plot the signal correlations as a function of distance of each pair of neurons.
In [77]:
Copied!
# Only use the upper half of the matrix due to symmetry of correlation
indices = numpy.triu_indices(number_of_rois, k=1)
matplotlib_pyplot.figure(figsize=(8,6))
matplotlib_pyplot.plot(distance[indices[0], indices[1]], signal_correlations[indices[0], indices[1]], ".", markersize=3.0)
matplotlib_pyplot.xlabel("Distance (pixels)", fontsize=16)
matplotlib_pyplot.ylabel("Signal correlation", fontsize=16);
# Only use the upper half of the matrix due to symmetry of correlation
indices = numpy.triu_indices(number_of_rois, k=1)
matplotlib_pyplot.figure(figsize=(8,6))
matplotlib_pyplot.plot(distance[indices[0], indices[1]], signal_correlations[indices[0], indices[1]], ".", markersize=3.0)
matplotlib_pyplot.xlabel("Distance (pixels)", fontsize=16)
matplotlib_pyplot.ylabel("Signal correlation", fontsize=16);
Conclusion
The Allen Brain Observatory - Visual Coding (Ophys) is a rich dataset that allows you to explore single cell and population responses across 6 cortical areas, 4 cortical layers, 14 transgenic Cre lines to a range of different visual stimuli.
This tutorial should enable you to get started in using these data including finding experiments and accessing the neural and behavioral data, along with stimulus information.
In [ ]:
Copied!